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研究生:簡才淦
研究生(外文):Tsai-Kan Chien
論文名稱:結合即時訊號頻譜與即時雜訊頻譜估計之語音增強演算法
論文名稱(外文):Improved speech enhancement with on-line signal and noise spectrum estimation
指導教授:吳國光吳國光引用關係
學位類別:碩士
校院名稱:國立中興大學
系所名稱:電機工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:96
語文別:中文
論文頁數:46
中文關鍵詞:語音增強頻譜失真Tho-LMS雜訊頻譜能量估計頻譜相減法Beta -最小均方誤差法重疊相加
外文關鍵詞:speech enhancementspectral distortionTho-LMSnoise spectrum estimationspectral subtractionBeta-order minimum mean squareoverlap and add
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本論文旨在研究如何把這些受到雜音干擾的訊號(noisy signal)把雜訊消除,讓接收端的訊號能盡量和原始傳送端語音訊號能夠相同,在增強語音抑制雜訊的同時,還要考慮降低頻譜失真度(spectral distortion)的現象,使收話者可以清楚得聽到傳話者要表達的辭句。在此提出 Tho-LMS方法先將受雜訊干擾的訊號作初步消除雜訊,之後經過傅利葉轉換轉成頻域,得到即時訊號頻譜,並利用雜訊頻譜能量估計的雜訊估測方法-相減法、非語音中斷法、最小統計法,再經過語音增強(speech enhancement)的方法把雜訊消除,傳統語音增強法如頻譜相減法(spectral subtraction,SS)、韋納濾波器(wiener filter)。在論文中則是使用另一種語音增強,稱為Beta-最小均方誤差法(Beta-MMSE),係將含雜訊訊號直接乘上一個含Beta變數的增益函數(gain function),此最佳Beta值是利用雜訊頻譜估計出的雜訊能量值計算出的音框訊雜比(frame SNR);最後,將其增強後的訊號作傅利葉反轉換回時域,並使用重疊相加(overlap and add),重建原始乾淨的語音訊號。
ABSTRACT

The thesis means how to reduce noise for noisy speech signal‚then received signal can approach transmitted clean signal. When reducing noise for speech enhancement‚it will still consider to reduce spectral distortion phenomenon. So the receiver clearly listens sentences that the transmitter wants to speak. This paper uses Tho-LMS method that reduces noise for noisy speech signal‚and gets on-line signal spectrum that transfers to frequency domain with Fourier transform. We use noise spectrum estimation methods to get noise energy spectrum, such as subtraction method‚non-speech pause method‚and minimum statistics method, that supply speech enhancement to reduce noise.
Traditional speech enhancement methods like spectral subtraction and Wiener filter. The paper uses the other speech enhancement that is called Beta-order minimum mean square. After noise spectrum estimation we get frame signal-to-noise ratio,it can be calculated optimum Beta variable. And the noisy speech signal is directly multiplied the gain function that includes Beta variable. Finally, the enhanced speech signal transfers to time domain with inverse Fourier transform, and we use overlap and add to get reconstructed clean speech signal.
摘要 i
Abstract ii
目次 iii
圖目次 v
表目次 vi
第一章 序論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究方法 1
1.4 章節介紹 2
第二章 適應性濾波器 3
2.1 LMS (Least Mean Square) 4
2.2 N-LMS (Normalized-Least Mean Square) 5
2.3 Tho-LMS Filter 演算法 6
2.4 改進 Tho-LMS Filter 演算法 10
第三章 雜訊頻譜估計 11
3.1 相減法 11
3.2 非語音中斷法(non-speech pause method,NSP) 12
3.3 最小統計法(minimum statistcs method,MS) 13
3.4 相減法與非語音中斷法並用 14
3.5 改良式最小遞迴平均 (improved minima controlled recursive averaging,IMCRA) 15
第四章 語音增強 18
4.1 最小均方誤差(Minimum Mean-Square Error Estimation) 18
4.2 適應性Beta階最小均方誤差估計 19
4.2.1 Beta階最小均方誤差估計的增益函數 19
4.2.2 適應性的Beta值 21
4.2.3 即時訊號頻譜估計 22
4.3 頻譜相減法(Spectral Subtraction) 24
4.3.1 Berouti’s Method 25
4.3.2 Modified Berouti’s Method 25
第五章 實驗結果與討論 27
5.1 含即時訊號頻譜估計的Beta-MMSE SNR改善 27
5.2 頻譜相減法 SNR改善 38
第六章 結論和未來展望 44
參考文獻 45
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[2] T. Shimamura and J. Yamauchi‚“Non-stationary noise estimation utilizing harmonic structure for spectral subtraction”Signals, Systems and Computers‚vol. 2‚pp. 2305- 2309‚Nov. 2004.
[3] R. Martin‚“Spectral subtraction based on minimum statistics”‚Proc.EUSIPCO 94‚pp. 1182-1185 , Sept.1994.
[4] 侯政宇,“使用Tho-LMS 於即時頻譜估計之語音增強研究”,國立中興大學電機工程學系碩士論文,2007
[5] W.R. Wu and P.C. Chen‚“Adaptive AR Modeling in White Gaussian Noise” IEEE Trans. Signal Processing‚vol. 45‚no. 5‚pp. 1184-1192‚May 1997.
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IEEE Consumer Electronics, 2001. ICCE. International
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